Enterprise Master Plan: Next-GenerationPlanning With Activity-Based Costing

Alan Dybvig, Jeff Karrenbauer, and John Miller

n years ago theConsortium ofAdvanced Man-

agement international(CAM-i), an acknowl-edged leader inactivity-based costing,published The ClosedLoop, 'one of the first bookson activity-based planning andbudgeting (ABPB). Since thattime, most activity-based cost-ing (ABC) software providershave added additional capabil-ity and application for ABPB.Starting with a fixed sales fore-cast and sales/marketing spend,almost all of the operatingdata req uired for the ABPB isreadily available in most ABCmodels.

What's been missing is theability to optimize a plan basedon a level of sales and market-ing spending that will provideboth the maximum profitand return on investment. Nolonger is that true. The nextgeneration is enterprise masterplanning (EMP), where boththe optimized forecast and sup-ply chain are solved simultane-ously to maximize profitabilityand the return on investment,

Specifically,the EMP is createdusing modelingsoftware that inte-grates three plan-ning techniques, allof which have been

Imagine relaxing the assumption of a fixed salesforecast to solve for the optimum level of sales andmarketing spending that will provide the maxi-mum profit and return on investment. This articleand case study explains how. © 2014 Wiley Petoolcss. Inc

© 2014 Wiley Periodicals, Inc.Published crane in Wiley Online Library {wileyonlinelibrary_coml001 10.1 002ljcaf.21958

as illustrated in the graphicrepresentation set forth inExhibit I.

There are five factors neces-sary for developing a maximallyprofitable annual plan:

I. The forecast must be vari-able in the driver-basedplan model.

2. The supply chain must bevariable in the driver-basedplan model.

3. The objective function(i.e., what you're tryingto optimize) must beprofit.

4. The solver must be pre-scriptive (vwhat is the bestX?") and not scenario anal-ysis ("What will happen ifwe do X?·').

5. The model must be solvedsimultaneously to developan EMP that incorporatesall five factors.

commercially avail-able for decades:

I. Supply chain networkdesign

2. Activity-based costing3. Marketing-mix modeling

The supply chain softwarerelaxes the assumption of a fixedsupply chain, uses profit as theobjective function, and bas aprescriptive solver. ABC modelsprovide the data for the costfunctions in an EMP by whichthe assumption of a fixed sup-ply chain are relaxed. ABC costfunctions are more convenientlydeveloped than those tradi-tionally developed and are thereason the case study examplecould be developed as describedbelow. Finally. the assumptionof a fixed sales (units) forecastis relaxed by response functionsdeveloped in traditional market-ing-mix modeling software.

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72 The Journal of Corporate Accounting & Finance / May/June 2014

<:i Exhibit 1 ~---

~

ENTERPRISE MASTER PLAN: Next Generation PlanningWouldn't You like to Know How Much Profit is Be!ng LeNon Ihe Tobie ThisYear?

An Enterprise Mosier Plan (EMP) Can Tell You.. and Assure it Never Happens Again

Optimizing Forecast

The resulting EMP math-ematically assures that the enter-prise's other planning applica-uons=e.g., financial planning& analysis (FP&A). sales andoperations planning (S&OP).and marketing mix modeling-are aligned to a maximally prof-itable forecast and an optimallyfeasible supply chain.

The power of an EMP wasconfirmed through a simpli-fied proof-of-concept (POC)case study based on ABC datapreviously developed by one ofthe authors. When optimized(and depending on the specificscenario), profit improvementsof 25 to 75 percent would havebeen possible. Thus, this model-ing can be justifiably describedas "revolutionary."

BACKGROUND

There are two ways to deter-mine the optimal solution forfinancial and operations plan-ning. One is termed descriptive(also referred to as scenarioanalysis or enumeration). It

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answers the question: What willhappen if we do X' The secondis termed prescriptive or norma-Tire. It answers the question:What is the best X) Normativetechniq ues are much more math-ematically sophisticated and arerequired when the number ofpossible scenarios is too numer-ous to analyze descriptively. Forany realistic and viable numberof possible alternatives. descrip-tive techniq ues are a suboptimalsolution.

Thus. rather than scenarioplanning to calculate X for eachindividual selected scenario. the[nture is about solving for thebest X by looking at every singlecombination of sales volumes,costs. constraints (e.g.. capacity)and sales and marketing spend-ing that results in the highestprofit and return on investment(ROil. In many ways. this is asgood as it gets for financial andoperations planning.

Optimizing profit and finan-cial return by relaxing both theassumptions of a fixed forecastand a fixed supply chain requires

an integration of predictive ana-lytics that describe how productsand services respond to mar-keting and sales spending (i.e.,response functions) and pre-scriptive mathematical program-ming techniques used for amongother things. supply chainnetwork design. The currentpractice of supply chain net-work design uses a mathematicalprogramming technique that isa mix of integer and linear pro-gramming (MILP). It has beenapplied successfully for morethan 40 years, since the commer-cialization of mathematical pro-gramming techniq ues developedoriginally by George Dantzig.

The MILP techniq ue hasbeen used to solve "long leadtime" (i.e., a year or more) sup-ply chain design questions suchas mergers; the number, loca-tion, and size of raw materialsuppliers; manufacturing facili-ties; production processes; distri-bution centers: and cross-docks.it has also been used to addressshorter lead time questions(i.e.. from days up to a year).

© 2014 Wiley Periodicals, Inc.

The Journal of Corporate Accounting & Finance / May/June 2014

Examples include capacity plan-ning, distribution methods andpolicies, and inventory analyses.

The open question iswhether the integration ofresponse functions into a sup-ply chain network design mod-eling software would work.Would it actually demonstratea profit and ROI improvement?Since the software to createan EMP existed, the questionquickly became would the datahave to be made up or did dataalready exist from which aProof of Concept (POC) casestudy model could be built.

The answer was a surprise toall the authors: an existing ABCmodel. This is because, as itturned out, the critical cost func-tions required to build the EMPwere readily created from previ-ously built ABC models. Thus,importantly, many of the exist-ing ABC models are ready-madefor building EMP case studyPOC model. This is becauseall the drivers in ABC modelsare units (i.e., activities). Theseforce the development of theassociated ABC planning factorsreq uired to drive the costs to theunits, including:

Activity consumption rate(ACR)Resource consumption rate(RCR)Cost factors (CF)

These planning factors areprecisely what an EMP modelneeds for its cost functions. Twoseemingly unrelated modelingtechniques, activity-based cost-ing and supply chain networkdesign, turned ou t to haveexactly the same cost-modelingarchitecture.

Thus, when CAM-I pub-lished its very detailed, ground-breaking book The ClosedLoop, describing how the ABC

© 2014 Wiley Periodicals, Inc.

planning factors (ACR, RCR,and CF) could be used for plan-ning purposes, the stage was set,10 years later, for the capabilityto optimize the closed loop byrelaxing its assumption of botha fixed forecast and a fixed sup-ply by applying MILP optimiza-tion techniques.

CASE STUDY: COMPANY ANDITS FINANCIALS

The POC case study was theMcCoy Belt Buckle Manufac-turing Company, which makesan iconic brand of belt bucklessold to distributors and corpo-rate customers located all overthe globe. Total annual produc-tion of belt buckles is about 17million. There are two lines ofbelt buckle products: standardand custom. Standard productsinclude tong, snap, and clip.Custom products start with astandard clip belt buckle thatis then engraved, embossed, ordecorated.

McCoy was selected for thiscase study because they had anexisting ABC system in placeand were willing to participateand be part of the project team.The thousands of productstock-keeping units (SKUs)were red uced to two broadproduct groups (i.e., custom andstandard), and three sales areas(North America, Europe/MiddleEast, and Far East). This, inturn. created the need for sixresponse functions, about whichMcCoy had some knowledgeand experience.

Like most companies,McCoy used the base-yearfinancial results as a startingpoint for developing the oper-ating plan and budgets for theupcoming year. An overall sum-mary of the McCoy reportedfinancial results for the base yearis set forth in Exhibit 2.

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Like most budgeting exer-cises, this was a bottom-up planthat started with a revenue pro-jection from the sales depart-ment. Operating managers usethis sales projection to developthe manufacturing plan. Thisbusiness is seasonal: A largepart of revenues is from ship-ments in the third quarter toretailers ramping up for theholiday season. Marketingand sales bases its spendingon estimates of promotionalmaterials, displays, incentives,and the advertising required toachieve the sales target. Generaland administration budgetsare made by managers basedprimarily on the previous yearswith adjustments to account forthe higher level of sales. Eachmanager prepares, reprepares,negotiates, and renegotiateshis individual budget, whichis complete when approved,Add it all up and you have thecompany operating plan andbudget.

Was it a good plan? Yes.Was it optimized for the highestprofit and ROI? No. The num-ber of possible solutions to anyrealistically sized model wouldgenerate an absurdly large num-ber of possible solutions thatwould have to be evaluated indi-vidually, by scenario analysis. Inoptimizing an operating plan,billions of combinations ofsales, costs, capacity, and salesand marketing spend are used toidentify those specific combina-tions that result in the highestprofits and ROJ.

The rest of this articledescribes how McCoy's activity-based costing data were used tobuild a simple functional modelof the base-year financial andoperational results, This model.in turn. when optimized. illus-trated profit opportunities inthe range of 25 to 75%. Profit

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Examples include capacity plan-ning, distribution methods andpolicies, and inventory analyses.

The open question iswhether the integration ofresponse functions into a sup-ply chain network design mod-eling software would work.Would it actually demonstratea profit and ROI improvement?Since the software to createan EMP existed, the questionquickly became would the datahave to be made up or did dataalready exist from which aProof of Concept (POC) casestudy model could be built.

The answer was a surprise toall the authors: an existing ABCmodel. This is because, as itturned out, the critical cost func-tions required to build the EMPwere readily created from previ-ously built ABC models. Thus,importantly, many of the exist-ing ABC models are ready-madefor building EMP case studyPOC model. This is becauseall the drivers in ABC modelsare units (i.e., activities). Theseforce the development of theassociated ABC planning factorsreq uired to drive the costs to theunits, including:

Activity consumption rate(ACR)

• Resource consumption rate(RCR)Cost factors (CF)

These planning factors areprecisely what an EMP modelneeds for its cost functions. Twoseemingly unrelated modelingtechniques, activity-based cost-ing and supply chain networkdesign, turned out to haveexactly the same cost-modelingarchitecture.

Thus, when CAM-I pub-lished its very detailed, ground-breaking book The ClosedLoop, describing how the ABC

© 2014 Wiley Periodicals, Inc.

planning factors (ACR. RCR,and CF) could be used for plan-ning purposes, the stage was set,10 years later, for the capabilityto optimize the closed loop byrelaxing its assumption of botha fixed forecast and a fixed sup-ply by applying MILP optimiza-tion techniques,

CASE STUDY: COMPANY ANDITS FINANCIALS

The POC case study was theMcCoy Belt Buckle Manufac-turing Company, which makesan iconic brand of belt bucklessold to distributors and corpo-rate customers located all overthe globe. Total annual produc-tion of belt buckles is about 17million. There are two lines ofbelt buckle products: standardand custom. Standard productsinclude tong, snap, and clip.Custom products start with astandard clip belt buckle thatis then engraved, embossed, ordecorated.

McCoy was selected for thiscase study because they had anexisting ABC system in placeand were willing to participateand be part of the project team.The thousands of productstock-keeping units (SKUs)were red uced to two broadproduct groups (i.e., custom andstandard), and three sales areas(North America, Europe/MiddleEast, and Far East). This, inturn. created the need for sixresponse functions, about whichMcCoy had some knowledgeand experience,

Like most companies,McCoy used the base-yearfinancial results as a startingpoint for developing the oper-ating plan and budgets for theupcoming year. An overall sum-mary of the McCoy reportedfinancial results for the base yearis set forth in Exhibit 2.

73

Like most budgeting exer-cises, this was a bottom-up planthat started with a revenue pro-jection from the sales depart-ment. Operating managers usethis sales projection to developthe manufacturing plan. Thisbusiness is seasonal: A largepart of revenues is from ship-ments in the third quarter toretailers ramping up for theholiday season. Marketingand sales bases its spendingon estimates of promotionalmaterials, displays, incentives,and the advertising required toachieve the sales target. Generaland administration budgetsare made by managers basedprimarily on the previous yearswith adjustments to account forthe higher level of sales. Eachmanager prepares, reprepares,negotiates, and renegotiateshis individual budget, whichis complete when approved.Add it all up and you have thecompany operating plan andbudget.

Was it a good plan? Yes.Was it optimized for the highestprofit and ROI? No. The num-ber of possible solutions to anyrealistically sized model wouldgenerate an absurdly large num-ber of possible solutions thatwould have to be evaluated indi-vidually, by scenario analysis. Inoptimizing an operating plan,billions of combinations ofsales, costs, capacity, and salesand marketing spend are used toidentify those specific combina-tions that result in the highestprofits and ROJ.

The rest of this articledescribes how McCoy's activity-based costing data were used tobuild a simple functional modelof the base-year financial andoperational results. This model.in turn. when optimized. illus-trated profit opportunities inthe range of 25 to 75%. Profit

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had in effect been left on thetable.

Sales

Total units for the base yearwere 16.5 million. and the aver-age selling price for all productswas $8.20. Selling prices forindividual products range from$7.05 to $10.65. The total num-ber of customers is about 2.500.mostly distributors who sell toretail stores. The largest area isthe United States, making upjust over half of total unit sales.The next largest is Europe/Mid-dle East, which together accountfor 28 percent of total sales. TheFar East region makes up theremaining sales.

Cost of Goods Sold

Direct material is about 72percent of the total cost of goodssold. All belt buckets go throughstandard manufacturing pro-cesses that include fabrication.buffing, and plating. Customizedbelt buckles require additionalmanufacturing processes forengraving, embossing. or fordecorating. The last manufactur-ing process is to package eachbelt buckle in a -l-by-o-inch box.Using ABC. the company wasable to define the cost of unit ofoutput for each of the manufac-turing processes. The combinedtotal of all manufacturing pro-cesses was $19.8 million.

Support processes (59.3million) included engineeringSUPpOIt(custom design. qualitycontrol/inspection, and materialtesting), manufacturing support(facility maintenance, machinemaintenance. custom machinemaintenance, product moves,and custom product moves),and manufacturing administra-tion (basic procurement. custom

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procurement. and productioncontrol).

Direct material and thecosts of each of the manufactur-ing and support processes wereexpressed as a cost per unit ofprocess/activity output, and eachwere rated for capacity and toidentify constraints. A determina-tion was made between fixed andvariable costs for each process.

Shipping and Warehouse

Shipping and warehousecosts were 53 million and$2 million. respectively Shippingcost per unit was calculated foreach of the six regions served.Most of the warehouse was usedfor customized products. Theexisting ABC system providedan accurate cost per unit for thewarehouse costs.

Sales and Marketing

Sales and marketing expenseswere $28 million, of which SI0million consisted mostly ofwages and commissions paid tosalespeople. each responsible fora specific territory. Most of theremaining $18 million representmarketing expenses in the formof rebates for marketing effortsperformed by the distributors.display units, local advertising.and for sponsorship of hunting,fishing. and rodeo events.

McCoy had considerablesales and marketing capabilitiesand significant historical data ontheir individual customers andthe success of marketing andsales expenditures. As part ofMcCoy's ABC system, they alsocalculated and reported the prof-itability of each of their 2,500customers. This information anddata played an important part inidentifying the response curvesused in the optimization model.

CASE STUDY MODEL DATA

As is true of any supplychain network design model, thePOC model we buil t is a seriesof geographically located nodesconnected by links arranged ina hierarchy, from procurementto customer. The nodes containfacilities, and within the facilities,activities and products. Thesenodes and links are appropriatelyconstrained (e.g., capacities).

However, the flows withinthe network (e.g, across a node,within a facility, or through anactivity) are not known, becausethey are the answer to the ques-tion: "What is the optimal sup-ply chain configuration to make.fulfill, and service the forecast?"Thus. the essential requirementfor optimized planning is anunderstanding of unit costs andhow they vary with volume. Aswill be described below. theserelationships are referred to ascost function curves.

While there are a variety ofinput data elements, the threekey ones for the McCoy casestudy model included cost func-tion curves, response functions,and capacity constraints.

Cost Function Curves

Cost function curves areexplicitly available from an ABCmodel. All the network costs inan EMP model must be repre-sented as cost functions. Costfunctions (as described by Dr.Charles Horngren, coauthorof the textbook Cost Account-ing, 12Th EdiTion [Prentice-Hall,2006, page 333]) are "descrip-tions of hall' {I cost changes withchanges in the level of (In activ-ity or volume relating to thatcost. "

Cost functions describe,mathematically, the relationship

© 2014 Wiley Periodicals, Inc.

The Journal of Corporate Accounting & Finance / May/June 2014

between volume changes (units,weight, or volume) and the costchanges driven by the volumechanges. These costs can varywith volume in a variety ofways, including linearly vari-able with increases or decreasesin volume, fixed costs thatdon't change with volume, orany combination of fixed andlinear.

Cost functions must be acombination of fixed or lin-early variable volumes, giventhe mathematical programmingtechnique employed (MILP, aspreviously described). Thus,plotting the cost function withchanges in cost on the y axis(the dependent variable) andchanges in units of volume onthe x axis (the independentvariable) yields the follow-ing arithmetic relationship:cost = slope x units. The slopeis expressed as cost/unit and isthe key mathematical factor inthe cost functions.

Th us, as described above, thetwo very different analytic tech-niques (ABC and supply chainnetwork design) have exactly thesame "key mathematical fac-tors." This, in a nutshell, is whysupply chain-based POC modelscan be easily created from ABCmodel data.

Thus, activity consumptionrate (ACR = activity/product)and resource consumptionrate (RCR = resource/activity)and the associated cost factor(CF = $/resource), when mul-tiplied were, in fact, preciselythe slope of the variable costfunctions req uired in the POCmodel. Thus, slope = activity/unit of product x resource/activity x $/resource = $/unitof product = slope of costfunction curve.

As described above, thesecosts can vary with volume in avariety of ways:

© 2014 Wiley Periodicals, Inc.

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~r:-------------------- -,oMcCoy Financial Results for Base Year ($ Millions)

Total Sales $135.3Expenses

Cost of Goods Sold 78.8Shipping/Warehouse 5.0Sales & Marketing 28.0G&A 10.0Total Expenses 121.8Operating Income $13.5

Linearly variable withincreases or decreases involume.Fixed costs that don'tchange with volume at all.Stepwise fixed.

Response Functions

Response functions havebeen around for decades andlink sales or marketing activitiesto revenue results. Specifically,they relax the assumption of afixed forecast by predicting reve-nues at different levels of sales ormarketing effort. Sales responsefunctions are used to drive salesforce resource optimization(SRO) while marketing responsefunctions are used in marketingmix modeling.

These relationships areused to inform critical resourceallocation decisions includinghow big the sales or market-ing budget should be. and towhich products and/or custom-ers should these resources beallocated. As a result. this pro-cess can lead to changes in indi-vidual product or customer

expenditures. In theseapproaches, the objective is tomaximize the contribution ofthe sales and marketing effortsafter accounting for the costsof these promotions and afixed product margin.

There are a broad range ofmethods that can be used to esti-mate enterprise response func-tions, which differ in the timeteffort involved and the precisionthat can be achieved. A partiallist of these methods includes:

In-market tests to isolatethe impact of individualpromotions.Econometric methods thatrely on statistical analysisto estimate the sales impactof prior sales and market-ing activities.Expert sessions that pro-vide a structured process tosolicit and refine estimatesof the impact that a pro-motion will have.

Regardless of how theresponse functions are derived.they can be compared toactual results and recalibrated

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as needed. This is analogousto the financial variance analy-sis process. They are the reverseof cost functions because theindependent variable is notunits but rather sales and mar-keting expenditures. The depen-dent variable is units. Units arealso frequently multiplied byprice-to-yield revenues as thedependent variable. This cre-ated the need for six responsefunctions about which McCoyhad some knowledge andexperience.

A significant advantage forbusiness-to-business firms (B2B)is that the requisite responsefunctions can be structuredeither to reflect the inventoryreplenishment demand of itscustomer or the final customerdemand of its customer's cus-tomers. Depending on thecircumstances, this could be asignificant planning advantagefor the B2B firm.

Capacity and OtherConstraints

All constraints. includingcapacities. must be identified.as they are an explicit require-ment for optimization. Further.while some constraints cannotbe relaxed (e.g., facility size).others can. These relaxations areincluded in the model. Examplesinclude:

Limits on procurementavailabilityManufacturing capacityDistribution Centerthroughput, storage

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Energy consumptionCarbon emissionsTargets for inventory andcustomer serviceTransportation linkrestrictionsSupply/demand imbalances(e.g., inventory build-aheadvs, overtime)Limits on sales and mar-keting expeditures

When appropriate. capacityrelief was included in the model.Most of the activities' capacitieswere relieved with labor in theform of new hires and temporaryworkers. Two. however. requiredadditional capital equipment.

CONCLUSIONS

EMP's functionality trulyrepresents next-generation ABCbased planning, both financiallyand operationally. it is. in effect.an optimization of the closedloop model CAM-i advanced inits 2004 book by that name.

Further. it is not "new" or"untested" analytics. Rather. itis simply the integration of twodifferent and robust sets of ana-lytics (i.e.. MILP and predictiveanalytics) that have been com-mercially successful for decades.

For firms whose experienceis with ABC modeling, the EMPPOC model is a platform fromwhich those firms can extendtheir operational uses of theABC data from efforts focusedon process improvements andcustomer/product profitabilityto annual financial and opera-tional planning applications like

forecasting and planning. WithABC data, an EMP POC modelcan be built with relatively littleadditional data gathering, asdescribed above, reducing themodel-build investment signifi-cantly.

No time need be spentat all descriptively, evaluat-ing alternative solutions viascenario analysis. EMP usesprescriptive techniques thatguarantee that the solutionis the very best one possible(i.e, optimal). It answers thequestion: "What is the bestX?" rather than the descri ptivetechnique, which answers thequestion: "What will happenif X is done?" Thus, the futureof financial and operationalplanning is about solving forthe best X by looking at everysingle combination of sales,costs. constraints (e.g., capac-ity). and sales and marketingspending that result in the max-imum profit, sales and market-ing ROt, and optimally feasiblesupply chain.

ACKNOWLEDGMENTS

The authors wish to thankGlenn Sabin, Managing Prin-cipal of ZS Associates' office inPrinceton, New Jersey, for hisessential contribution to oureffort-specifically, the techni-cal practicality of enterpriseresponse functions.

NOTEI. See http://www.ca1l1-i.orgJwiki/index

.phpllmCL_ TableOfColltents

© 2014WileyPeriodicals,Inc.

The Journal of Corporate Accounting & Finance / May/June 2014

Alan Dybvig is managing partner of Dybvig Consulting, a consulting firm specializing in the application ofpredictive analytics and prescriptive techniques to create a single enterprise master plan. He can be reachedat alan@dybvigconsulting.com.Jeff Karrenbauer is president of INSIGHT, Inc., a world leader in providingsupply chain software and consulting services, He can be reached at jkarrenbauer@insightoutsmart.com"John Miller is president of Arkonas a management consulting firm specializing in activity-based costingand product/service/customer costing and profitability. He can be reached at jmiller@arkonas.com.

© 2014 Wiley Periodicals. Inc.

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